Previous models [1]}, [2]} used two separate architectures with soft parameter sharing.
They used cross-stitch units or task transfer connections to leverage the knowledge of the task-specific networks.
In contrast, MRN [3]} learned a Bayesian transfer relationship (both the last layers).
The previous models, having their own parameters for each task, made it easy to increase the number of required resources.
